fix: refactor adapter weight loading and mapping

launcher/src/main.rs

@@ -1592,6 +1592,10 @@ fn main() -> Result<(), LauncherError> {
     // Download and convert lora adapters if any
     if let Some(lora_adapters) = &args.lora_adapters {
         for adapter in lora_adapters.split(',') {
+            // skip download if a path is provided
+            if adapter.contains('=') {
+                continue;
+            }
             download_convert_model(
                 adapter,
                 None,

server/tests/utils/test_adapter.py

@@ -0,0 +1,187 @@
+import pytest
+from unittest.mock import Mock
+from text_generation_server.utils.adapter import get_attn_weights, get_mlp_weights
+
+
+def test_get_attn_weights():
+    # create a mock layer
+    mock_layer = Mock()
+    mock_layer.self_attn.query_key_value = Mock()
+    mock_layer.self_attn.o_proj = Mock()
+
+    # call the function
+    result = get_attn_weights(2, mock_layer)
+
+    # assert the result
+    expected = {
+        (2, "q_proj"): (
+            "model.layers.2.self_attn.q_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "k_proj"): (
+            "model.layers.2.self_attn.k_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "v_proj"): (
+            "model.layers.2.self_attn.v_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "o_proj"): ("model.layers.2.self_attn.o_proj", mock_layer.self_attn.o_proj),
+    }
+    assert result == expected
+
+
+def test_get_mlp_weights_with_gate_up_proj():
+    # create a mock layer with gate_up_proj
+    mock_layer = Mock()
+    mock_layer.mlp.gate_up_proj = Mock()
+    mock_layer.mlp.down_proj = Mock()
+
+    # call the function
+    result = get_mlp_weights(3, mock_layer)
+
+    # assert the result
+    expected = {
+        (3, "gate_proj"): ("model.layers.3.mlp.gate_proj", mock_layer.mlp.gate_up_proj),
+        (3, "up_proj"): ("model.layers.3.mlp.up_proj", mock_layer.mlp.gate_up_proj),
+        (3, "down_proj"): ("model.layers.3.mlp.down_proj", mock_layer.mlp.down_proj),
+    }
+    assert result == expected
+
+
+def test_get_mlp_weights_without_gate_up_proj():
+    # create a mock layer without gate_up_proj
+    mock_layer = Mock()
+    mock_layer.mlp = Mock(spec=[])
+
+    # call the function
+    result = get_mlp_weights(1, mock_layer)
+
+    # assert the result
+    assert result == {}
+
+
+@pytest.mark.parametrize("layer_index", [0, 1, 5])
+def test_get_attn_weights_different_layers(layer_index):
+    mock_layer = Mock()
+    mock_layer.self_attn.query_key_value = Mock()
+    mock_layer.self_attn.o_proj = Mock()
+
+    result = get_attn_weights(layer_index, mock_layer)
+
+    for k in ["q", "k", "v"]:
+        assert (layer_index, f"{k}_proj") in result
+        assert (
+            result[(layer_index, f"{k}_proj")][0]
+            == f"model.layers.{layer_index}.self_attn.{k}_proj"
+        )
+
+    assert (layer_index, "o_proj") in result
+    assert (
+        result[(layer_index, "o_proj")][0]
+        == f"model.layers.{layer_index}.self_attn.o_proj"
+    )
+
+
+@pytest.mark.parametrize("layer_index", [0, 1, 5])
+def test_get_mlp_weights_different_layers(layer_index):
+    mock_layer = Mock()
+    mock_layer.mlp.gate_up_proj = Mock()
+    mock_layer.mlp.down_proj = Mock()
+
+    result = get_mlp_weights(layer_index, mock_layer)
+
+    for k in ["gate", "up", "down"]:
+        assert (layer_index, f"{k}_proj") in result
+        assert (
+            result[(layer_index, f"{k}_proj")][0]
+            == f"model.layers.{layer_index}.mlp.{k}_proj"
+        )
+
+
+def test_get_attn_weights_llama_compatibility():
+    mock_layer = Mock()
+    mock_layer.self_attn.query_key_value = Mock()
+    mock_layer.self_attn.o_proj = Mock()
+
+    result = get_attn_weights(2, mock_layer)
+
+    expected = {
+        (2, "q_proj"): (
+            "model.layers.2.self_attn.q_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "k_proj"): (
+            "model.layers.2.self_attn.k_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "v_proj"): (
+            "model.layers.2.self_attn.v_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "o_proj"): ("model.layers.2.self_attn.o_proj", mock_layer.self_attn.o_proj),
+    }
+    assert result == expected
+
+
+def test_get_mlp_weights_llama_compatibility():
+    mock_layer = Mock()
+    mock_layer.mlp.gate_up_proj = Mock()
+    mock_layer.mlp.down_proj = Mock()
+
+    result = get_mlp_weights(3, mock_layer)
+
+    expected = {
+        (3, "gate_proj"): ("model.layers.3.mlp.gate_proj", mock_layer.mlp.gate_up_proj),
+        (3, "up_proj"): ("model.layers.3.mlp.up_proj", mock_layer.mlp.gate_up_proj),
+        (3, "down_proj"): ("model.layers.3.mlp.down_proj", mock_layer.mlp.down_proj),
+    }
+    assert result == expected
+
+
+def test_get_attn_weights_gemma_compatibility():
+    mock_layer = Mock()
+    mock_layer.self_attn.query_key_value = Mock()
+    mock_layer.self_attn.o_proj = Mock()
+
+    result = get_attn_weights(2, mock_layer)
+
+    expected = {
+        (2, "q_proj"): (
+            "model.layers.2.self_attn.q_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "k_proj"): (
+            "model.layers.2.self_attn.k_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "v_proj"): (
+            "model.layers.2.self_attn.v_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
+        (2, "o_proj"): ("model.layers.2.self_attn.o_proj", mock_layer.self_attn.o_proj),
+    }
+    assert result == expected
+
+
+def test_get_mlp_weights_gemma_compatibility():
+    mock_layer = Mock()
+    mock_layer.mlp.gate_proj = Mock()
+    mock_layer.mlp.up_proj = Mock()
+    mock_layer.mlp.down_proj = Mock()
+
+    # ensure that the mock_layer.mlp.gate_up_proj attribute does not exist.
+    # This is necessary because the use of `Mock` automatically creates any
+    # attributes that are accessed, even if they don't exist in the actual
+    # implementation. If `gate_up_proj` were created, `get_mlp_weights` might
+    # follow the wrong execution path and return an incorrect result.
+    del mock_layer.mlp.gate_up_proj
+
+    result = get_mlp_weights(3, mock_layer)
+
+    expected = {
+        (3, "gate_proj"): ("model.layers.3.mlp.gate_proj", mock_layer.mlp.gate_proj),
+        (3, "up_proj"): ("model.layers.3.mlp.up_proj", mock_layer.mlp.up_proj),
+        (3, "down_proj"): ("model.layers.3.mlp.down_proj", mock_layer.mlp.down_proj),
+    }
+    assert result == expected

server/text_generation_server/adapters/config.py

@@ -4,7 +4,7 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Dict, Optional, Set, Tuple
+from typing import TYPE_CHECKING, Dict, Set, Tuple
 
 import torch
 
@@ -31,14 +31,3 @@ def map_weights_for_model(
         weight_names: Tuple[str],
     ) -> Tuple[ModuleMap, Set[str]]:
         pass
-
-    @abstractmethod
-    def load_batched_adapter_weights(
-        self,
-        model: "Model",
-        module_map: ModuleMap,
-        layer_type: str,
-        unused_weight_names: Set[str],
-        dynamic: bool,
-    ) -> Optional[AdapterWeights]:
-        pass

server/text_generation_server/adapters/lora.py

@@ -102,22 +102,6 @@ def map_weights_for_model(
             adapter_weight_names.add(lora_b_name)
         return module_map, adapter_weight_names
 
-    def load_batched_adapter_weights(
-        self,
-        model: "Model",
-        module_map: Dict[str, Dict],
-        layer_type: str,
-        unused_weight_names: Set[str],
-        dynamic: bool,
-    ) -> Optional[AdapterWeights]:
-        return LoraWeights.load(
-            self,
-            model,
-            module_map,
-            layer_type,
-            unused_weight_names,
-        )
-
     @classmethod
     def load(cls, adapter_id: str, api_token: str) -> "LoraConfig":
         hf_config = _LoraConfig.from_pretrained(adapter_id, token=api_token)
@@ -192,22 +176,38 @@ def _transpose_weights(self):
     def get_batch_types(cls) -> List[Type[BatchAdapterWeights]]:
         return [BatchLoraWeights]
 
+    # prepare pre-loaded lora weights for use in the model.
+    #
+    # this method processes and organizes lora weights for a specific layer type across all layers:
+    # - uses `config` (LoraConfig) to apply lora-specific settings like scaling factor.
+    # - retrieves weights from `module_map` based on the `layer_type`.
+    # - processes `nlayers` number of layers.
+    # - converts weights to the specified `dtype`.
+    # - shards weights across `world_size` number of processes using the `process_group`.
+    # - maps weights to specific layers using `target_to_layer`.
+    # - tracks `unused_weight_names` to identify any unused weights.
+    #
+    # the method handles weight transposition, scaling, and padding to ensure compatibility
+    # with SGMV or BGMV operations.
     @classmethod
-    def load(
+    def prepare_weights(
         cls,
         config: LoraConfig,
-        model: "Model",
         module_map: Dict[str, Dict],
         layer_type: str,
         unused_weight_names: Set[str],
+        nlayers: int,
+        dtype: torch.dtype,
+        world_size: int,
+        process_group: ProcessGroup,
+        target_to_layer: Dict[str, Tuple[str, torch.Tensor]],
     ) -> Optional[AdapterWeights]:
-        nlayers = model.get_num_layers_for_type(layer_type)
         lora_a_list = [None] * nlayers
         lora_b_list = [None] * nlayers
 
         for layer_id in range(nlayers):
             key = (layer_id, layer_type)
-            weight_name, layer = model.target_to_layer[key]
+            weight_name, layer = target_to_layer[key]
             base_weight = layer.base_layer.linear.weight
             base_device = base_weight.device
 
@@ -216,10 +216,10 @@ def load(
                 return None
 
             lora_a, lora_a_name = module_map[weight_name]["lora_A"]
-            lora_a = lora_a.to(base_device, model.dtype)
+            lora_a = lora_a.to(base_device, dtype)
 
             lora_b, lora_b_name = module_map[weight_name]["lora_B"]
-            lora_b = lora_b.to(base_device, model.dtype)
+            lora_b = lora_b.to(base_device, dtype)
 
             scale = get_scaling_factor(
                 config.lora_alpha,
@@ -236,12 +236,8 @@ def load(
             lora_b_list[layer_id] = lora_b.transpose(0, 1) * scale
 
         # pad lora ranks to be compatible with sgmv
-        lora_a_list = [
-            pad_rank(w, dim=1, world_size=model.world_size) for w in lora_a_list
-        ]
-        lora_b_list = [
-            pad_rank(w, dim=0, world_size=model.world_size) for w in lora_b_list
-        ]
+        lora_a_list = [pad_rank(w, dim=1, world_size=world_size) for w in lora_a_list]
+        lora_b_list = [pad_rank(w, dim=0, world_size=world_size) for w in lora_b_list]
 
         if lora_a_list:
             # update rank if it was padded
@@ -252,8 +248,8 @@ def load(
             *shard_lora_weights(
                 weights_a=lora_a_list,
                 weights_b=lora_b_list,
-                split_dim=0 if model.is_row_parallel(layer_type) else 1,
-                process_group=model.process_group,
+                split_dim=0 if layer_type in {"o_proj", "down_proj", "lm_head"} else 1,
+                process_group=process_group,
             ),
             config,
         )
@@ -293,10 +289,6 @@ def can_vectorize(self, pg: ProcessGroup) -> bool:
             for rank_data in self.rank_data.values()
         )
 
-    @classmethod
-    def key(cls) -> str:
-        return "lora"
-
     @classmethod
     def load(
         self,

server/text_generation_server/adapters/weights.py

@@ -42,10 +42,6 @@ class BatchAdapterWeights(ABC):
     def has_adapter(self, adapter_index: int) -> bool:
         pass
 
-    @abstractclassmethod
-    def key(cls) -> str:
-        pass
-
     @abstractclassmethod
     def load(
         cls,
@@ -71,13 +67,6 @@ def remove_adapter(self, adapter_idx: int):
             return
         del self.adapter_weights[adapter_idx]
 
-    @property
-    def max_speculative_tokens(self) -> int:
-        return max(
-            adapter_weights.speculative_tokens
-            for adapter_weights in self.adapter_weights.values()
-        )
-
     def is_empty(self) -> bool:
         return len(self.adapter_weights) == 0
 
@@ -101,7 +90,7 @@ def get_data(
                 adapter_weights, meta, prefill, prefill_head_indices
             )
             if batched_weights is not None:
-                batch_data[batch_type.key()] = batched_weights
+                batch_data = batched_weights
         return batch_data
 
 
@@ -133,8 +122,7 @@ def from_meta(
     def ranks(self) -> Set[int]:
         # TODO(travis): refactor to be less coupled to lora implementation
         ranks = set()
-        for layer_data in self.data.values():
-            lora_data = layer_data.get("lora")
+        for lora_data in self.data.values():
             if lora_data is None:
                 continue